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opencsg-CodeLlama-7b-v0.1

Text Generation
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llama
llama-2
text-generation-inference
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AmeliaYin commited on
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Update README.md

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@@ -60,11 +60,11 @@ To simplify the comparison, we chosed the Pass@1 metric for the Python language,
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  | Model | HumanEval python pass@1 |
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  | --- |----------------------------------------------------------------------------- |
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  | CodeLlama-7b-hf | 30.5%|
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- | opencsg-CodeLlama-7b-v0.1(4k) | **42.7%** |
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  | CodeLlama-13b-hf | 36.0%|
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- | opencsg-CodeLlama-13b-v0.1(4k) | **45.1%** |
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  | CodeLlama-34b-hf | 48.2%|
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- | opencsg-CodeLlama-34b-v0.1(4k)| **48.8%** |
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  **TODO**
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  - We will provide more benchmark scores on fine-tuned models in the future.
@@ -185,11 +185,11 @@ HumanEval 是评估模型在代码生成方面性能的最常见的基准，尤
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  | 模型 | HumanEval python pass@1 |
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  | --- |----------------------------------------------------------------------------- |
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  | CodeLlama-7b-hf | 30.5%|
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- | opencsg-CodeLlama-7b-v0.1(4k) | **42.7%** |
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  | CodeLlama-13b-hf | 36.0%|
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- | opencsg-CodeLlama-13b-v0.1(4k) | **45.1%** |
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  | CodeLlama-34b-hf | 48.2%|
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- | opencsg-CodeLlama-34b-v0.1(4k)| **48.8%** |
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  **TODO**
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  - 未来我们将提供更多微调模型的在各基准上的分数。
 
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  | Model | HumanEval python pass@1 |
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  | --- |----------------------------------------------------------------------------- |
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  | CodeLlama-7b-hf | 30.5%|
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+ | opencsg-CodeLlama-7b-v0.1(4k) | **43.9%** |
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  | CodeLlama-13b-hf | 36.0%|
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+ | opencsg-CodeLlama-13b-v0.1(4k) | **51.2%** |
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  | CodeLlama-34b-hf | 48.2%|
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+ | opencsg-CodeLlama-34b-v0.1(4k)| **56.1%** |
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  **TODO**
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  - We will provide more benchmark scores on fine-tuned models in the future.
 
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  | 模型 | HumanEval python pass@1 |
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  | --- |----------------------------------------------------------------------------- |
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  | CodeLlama-7b-hf | 30.5%|
188
+ | opencsg-CodeLlama-7b-v0.1(4k) | **43.9%** |
189
  | CodeLlama-13b-hf | 36.0%|
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+ | opencsg-CodeLlama-13b-v0.1(4k) | **51.2%** |
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  | CodeLlama-34b-hf | 48.2%|
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+ | opencsg-CodeLlama-34b-v0.1(4k)| **56.1%** |
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  **TODO**
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  - 未来我们将提供更多微调模型的在各基准上的分数。